I am a member of the Machine Learning and Optimization and the Algorithms and Data Sciences Group at Microsoft Research, Bangalore, India. My research interests are in machine learning, large-scale (non-convex optimization), and statistical learning theory. I am also interested in applications of machine learning to privacy, computer vision, text mining and natural language processing. I completed my PhD at the University of Texas at Austin under Prof. Inderjit S. Dhillon.

I am also an adjunct faculty member at IIT Kanpur.

Professional Services

  • Organization:
    • Program Co-Chair, IKDD Conference on Data Sciences (CoDS), 2016.
    • Organizer, Machine Learning Summer School, Microsoft Research, 2015.
    • Organizer, Mysore Park Workshop on Machine Learning, Mysore, India, 2012.
  • Program Committee/Area Chair
    • COLT 2015, 2016
    • NIPS 2012, 2013, 2016


Over the years, I have been very lucky to have worked with some amazing postdocs/interns/research fellows.


  • Purushottam Kar, 2013-2015 (Asst. Prof., IIT Kanpur)
  • Nagarajan Natarajan, 2015- (Postdoc, MSR India)


  • Elena-Madalina Persu, Summer’2015. (Phd Student, MIT)
  • Gautam Kamath, Summer’2015. (Phd Student, MIT)
  • Harikrishna Narasimhan, Summer’2014. (Postdoc, Harvard University)
  • Praneeth Netrapalli, Summer’2012, 2014. (Postdoc, Microsoft Research New England)
  • Srinadh Bhojanapalli, Summer’2013, 2014. (Assistant Professor, TTI Chicago)
  • Pravesh Kothari, Summer’2014. (Phd Student, UT Austin)
  • Purushottam Kar, Summer’2012. (Assistant Professor, IIT Kanpur)
  • Sivakanth Gopi, Summer’2012. (Phd Student, Princeton University)
  • Ankan Saha, Summer’2011. (Software Engineer, LinkedIn)
  • Saurabh Gupta, Summer’2011. (Phd Student, UC Berkeley)

Research Fellows:

  • Kush Bhatia, 2014-2016. (PhD Student, UC Berkeley)
  • Yeshwanth Cherapanamjeri, 2015-. (RF, MSR India)
  • Raajay Viswanathan, 2011-2013. (PhD Student, UWisc Madison)


Provable Non-convex Optimization for Machine Learning Problems

Established: April 4, 2014

In this work, we explore theoretical properties of simple non-convex optimization methods for problems that feature prominently in several important areas such as recommendation systems, compressive sensing, computer vision etc. Talks: Provable Non-convex Optimization for Machine Learning. Summer School on…


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Memory Limited, Streaming PCA
Ioannis Mitliagkas, Constantine Caramanis, Prateek Jain, in Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States., December 1, 2013, View abstract, View external link






Panel Q and A Link description

Panel Q and A


July 27, 2015


Prateek Jain, Chin-Jen Lin, Aditya Gopalan, Suvrit Sra, and Stefanie Jegelka


Microsoft, National Taiwan University, IISc, Max Planck Institute for Intelligent Systems, UC Berkeley



  • Non-convex Optimization for High-Dimensional Statistics
  • Matrix Completion and Low-rank Matrix Recovery
  • Compressive Sensing
  • Learning with Non-decomposable Loss Functions
  • Differential Privacy for Machine Learning
  • Distance Metric Learning